pulp

I am trying to solve the Bin Packing Problem using the Integer Programming Formulation in Python PuLP. The model for the problem is as follows: I have written the following Python Code using the PuLP library from pulp import * #knapsack problem def knapsolve(bins, binweight, items, weight): prob = LpProblem('BinPacking', LpMinimize) y = [LpVariable("y{0}".format(i+1), cat="Binary") for i in range(bins)] xs = [LpVariable("x{0}{1}".format(i+1, j+1), cat="Binary") for i in range(items) for j in range(bins)] #minimize objective nbins = sum(y) prob += nbins print(nbins) #constraints prob += nbins >

I was trying to resolve a LP problem with a constraint that is calculated by dividing variable A by variable B. The simple version of the problem is as below: The product is made by two materials (A and B) % of A should be greater than 50% % of B should be less than 40% Total amount of A and B are 100 Objective: What's the minimum amount of A? The code is like: from pulp import * prob = LpProblem('Simple problem', LpMinimize) x = LpVariable('x', 0, None, 'Integer') y = LpVariable('y', 0, None, 'Integer') prob += x prob += x / (x + y) > 0.5 # <== Where the error happens prob += y / (x + y) < 0

I am trying to install "pulp" module in Anaconda Navigator's Environment tabs. But when I search in "All" packages I can't find it. It happened with other packages too. Is there any way to install my package to the desired environment? I tried to install it by opening a terminal in the environment, but I see that afterwards it won't show up in the list. What am I missing here? Lin Eliza Click Open terminal from environment Execute conda install keras in terminal mode Download tar.gz file of the package (in your case, pulp) from here and follow the accepted answer of this stackoverflow question

In Python only, and using data from a Pandas dataframe, how can I use PuLP to solve linear programming problems the same way I can in Excel? How much budget should be allocated to each Channel under the New Budget column so we maximize the total number of estimated successes? I'm really looking for a concrete example using data from a dataframe and not really high-level advice. Problem Data Setup Channel 30-day Cost Trials Success Cost Min Cost Max New Budget 0 Channel1 1765.21 9865 812 882.61 2647.82 0 1 Channel2 2700.00 15000 900 1350.00 4050.00 0 2 Channel3 2160.00 12000 333 1080.00 3240.00

I'm trying to use PuLP, but it is taking 50 seconds to add 4000 constraints (with 67 variables). Solving the problem only takes a fraction of a second. We want to use PuLP to easily test several solvers on a large set of problems. Should it be taking PuLP this long? Using PyGLPK directly takes only a fraction of second including both setup and solving, so I hope not. What can I do to improve the efficiency of this step in PuLP? Update My constraints matrix is very sparse, and I was able to reduce the setup time to 4 or 5 seconds for this particular problem by only including nonzero